Counterfactual learning [1]} is a key direction of the research on causal inference [2]}[3]}.
Counterfactual learning aims to simulate counterfactuals to alleviate the missing-not-at-random bias for a less biased model training [4]}.
In the context of credit scoring, we know that rejected samples are unobserved, but counterfactual learning tries to answer “what if they are observable?”
